Metacurate-ML: Metadata Extraction from CAIs
Year: 2024
Type: article
Abstract: Extending the results of our work on pre-trained language models with recent developments in text-layout models and zero-shot techniques. Since relying solely on textual information makes it difficult to accurately classify and extract metadata, a combination of textual content and visual logic that incorporates vision transformers with optimisation techniques will be explored. This will allow us to extract the specific items with questionnaires such as question texts, responses and routing to create a rich source of metadata which provenances’ data collection methodology to the resultant data which can be transformed into DDI-Lifecycle. We will investigate the feasibility of document understanding multimodal models that employ masked language techniques and present the resulting challenges.
Authors: Pravin, Chandresh, De, Suparna, Wang, Zeqiang, Lungley, Deirdre, Bradshaw, Paul, Johnson, Jon
Institutions: University of Surrey, University College London
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G6972678495, G7483196170
OpenAlex ID: W6912037172
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